O1O: Grouping of known classes to identify odd-one-out

Object detection methods trained on a fixed set of known classes struggle to detect objects belonging to unknown classes in real-world scenarios. Open-world methodologies have emerged in recent years as a solution for the limitations of closed-set approaches. The main goal of open-world object detection is to detect and identify novelties while maintaining closed-set abilities. One common approach involves incorporating approximate supervision with pseudo-labels corresponding to candidate locations of objects, typically obtained in a class-agnostic manner. While previous attempts mainly rely on the appearance of objects, we propose that geometric cues provide a better solution as the source of pseudo-labels. By considering not just how objects look but also their shapes and relative locations, we aim to improve the system's ability to detect unfamiliar objects. Although additional supervision from pseudo-labels improves unknown object detection, it also introduces confusion for known classes. We observed a notable decline in the model's performance for detecting known objects in the presence of noisy pseudo-labels. To address this problem, we drew inspiration from human cognitive science. Studies about how humans mentally represent objects found that humans group objects based on their common attributes, which then helps to compare and identify the different ones given a group of objects. We applied a similar concept by organizing known object classes into a smaller set of superclasses by learning discriminative superclass representations. By doing so, our model can identify similarities between classes within a superclass, thereby facilitating the detection of unknown classes through an odd-one-out scoring mechanism. Our experiments on open-world detection benchmarks demonstrate significant improvements in unknown recall consistently across all tasks. Crucially, we achieve this without compromising known performance, thanks to better partitioning of the feature space with superclasses.